Visual Compositional Data Analytics for Spatial Transcriptomics

TL;DR

This paper introduces a visual analytics system for spatial transcriptomics data that integrates compositional data analysis with linked visualizations to facilitate pattern discovery and spatial understanding.

Contribution

It presents a novel visual analytics system combining compositional data analysis and linked views for spatial transcriptomics, enhancing pattern recognition and spatial interpretation.

Findings

Effective exploration of cell type patterns in tissue

Integration of Aitchison geometry improves clustering accuracy

Linked views facilitate spatial pattern discovery

Abstract

For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics data. Our design uses three linked views: a view of the histological image of the tissue, a stacked bar chart showing cell type proportions of the spots, and a scatter plot showing a dimensionality reduction of the multivariate proportions. Furthermore, we apply a compositional data analysis framework, the Aitchison geometry, to the proportions for dimensionality reduction and $k$-means clustering. Leveraging brushing and linking, the system allows one to explore and uncover patterns in the cell type mixtures and relate them to their spatial locations on the cellular tissue. This redesign shifts the pattern recognition workload from the human visual system to computational methods commonly used in visual analytics. We provide the code and setup instructions of our visual analytics system on GitHub (https://github.com/UniStuttgart-VISUS/va-for-spatial-transcriptomics).